proc0

I'm not saying nobody cares right now. I'm saying nobody would care if things were better, and part of that evidence is that even now when things are bad there is still a sizeable portion of the population that don't care. By your source there it's still more than a third of people don't think it's important.

I'm also talking about a hypothetical where most people would fit into the middle class. If we eliminate the entire bottom tier of economic status, don't you think that would have an impact on how people see the ultra-rich? It's not the gap that's important, it's whether the bottom tier of people are homeless or living in a house with a job they can live with.

Part of the evidence is looking at who is even politically engaged. Most people are not. Do you think if people had a few hundred thousand in the bank they would care about millionaires or billionaires making too much money? I certainly wouldn't and doubt most would.

People don't care about others if they are living well. They accomplish this if there is a good job market with a lot of options that favors workers instead of corporations. It only gets really bad because corporations have too much leverage because no one can compete. They become the only option, and can dictate salaries and job availability.

The only way this is not the case is if you assert that most people are not good enough to compete. That's an ironic stance because the excuse for wealth redistribution becomes about inherent inequality, like poor people can't really be competitive even if the rules of the game favor them.

Right, so there could be laws that benefit the wealthy and they are extracting from everyone else. Probably the best way is to avoid all of them, have simple laws for everyone.

And that pressure valve can backfire, the wealthy move their money, future investments don't happen, etc. The imbalances should be addressed by lowering the bar for competition, and this is what the government seems to never want to do. Taxing the rich is a distraction.

You're right, I meant extracting wealth beyond taxes, although certainly taxes can also be used for wealth extraction. Having funds for public benefit is not extracting wealth, so the line is blurry but we can look at how effective the system is that needs this money and if it's not effective then what else can it be other than extracting wealth.

Skynet finally? (Terminator movie used Oracle building as Skynet HQ, forgot which one)

Laws should never be used to extract wealth. Whatever the government invests effort it, it specializes in that over time and it entrenches itself. Laws and government should only exist to even the playing field of the market, allowing small businesses to rise faster.

This would be just as bad for the rich since their businesses would have more competition. The big difference is that tax goes through government, allowing politicians to have a huge incentive that benefits them, whether it's through legal means or not.

I think that will be true in the future, or maybe it's true within certain domains, but I believe everyone is still reading AI output and double checking it.

My point is that to the extent AI becomes as effective as its meant to be by design, it will completely eliminate even thinking in those higher abstractions. It will do the abstractions for you.

It's a minor point, and I get the general picture. AI is allowing very high level use of the computer. However, the distinction means AI's effectiveness comes from taking away the use programming languages, to the extent it is doing what it was designed to do.

This is why I believe thinking it's empowering engineering is accidental to AI NOT working well, because it wasn't design to empower as much as replace engineering.

Programming abstraction vs. general process or work abstraction. The distinction is relevant because AI is using the same programming abstractions as humans, that's why it's so useful.

That was true when implementation was expensive. Now that implementation is cheap, doing it in bulk is the best way to maximize AI usage, and that means fewer engineers doing more of the technical work.

> unless AI is directed at finding the right problems to solve

This direction can be done by non-engineers with AI to consult (replacing that part of human engineer's role).

Ai is not a programming abstraction, just like hiring a contractor is not a programming abstraction. Taking a few lines of code and throwing them into a function is a programming abstraction. It's a kind of mapping, whether it's full of side effects or not.

AI is meant to do the work for you. That is like hiring someone, which is not an abstraction, and also why so far we have to check what it does in ways that is different than checking Assembly after compilation. One is about optimization that isn't often needed, the other is about double checking the result actually worked.

I'm just speculating why the current structure of companies is not maximizing what AI can offer. Until it becomes completely self-directed and reliable, using a tool that allows you to move fast requires more technical knowledge, not less. What you're missing from your example is releasing to production without breaking it, dealing with increasing complexity as more features are added, maintaining development pace as more features are added, and trying not to create more bugs than are fixed.

The idea is more or less benefiting from doing work in bulk which AI is now allowing. I even think multiple engineers (human redundancy to double check the same code) should team up with one AI system and form a small team that handles millions of lines of code, with the only job to feed incoming tickets and check the AI work. Anyone not reviewing code should be facilitating everything else with their own AIs. I think large companies can collapse entire engineering departments into a few people. The cost would be that everyone else has to think like an engineer to sort out requirements and provide direction, but as you point out, this is now easier with the help of AI.

I'm talking about the day to day work. At least in my experience at several big tech companies, the more senior an engineer is, the more you need to delegate work and are expect to lead projects by managing them. There's technical knowledge involved of course but it's a small part of it, mostly directing other more junior engineers.

AI allows you to move faster, but it also demands that you pay more attention in proportion to how fast you want to move. This requires more focus, i.e. more time developing. Effectively the structure at companies should swing back to how it used to be a few decades ago (I think, maybe I'm wrong and it's always been like this), where engineers mostly focused on delivering code. The difference now is that they can be expected to deliver much more code at higher quality.

To summarize, using a little AI doesn't pay off, you need to be doing that all day, and that is not how engineers spend their time in most of the industry anymore.

I suspect the reason is the industry just does not want engineers to be mostly technical. There's a real tradeoff to AI, and I think companies are taking the wrong lesson and thinking "well now that programming is so easy, engineers need to do more of XYZ", when I think it should be "this tool is powerful but also takes more focus so engineers should do less of XYZ".

The creative space. It is also being underestimated. Lots of people saying AI will now make movies etc, but I think anything of value in this space will require heavy human intervention for a while.

In more technical areas, it will be more of an exception but humans can still provide precise control over software that in certain areas are desirable, i.e. critical safety, embedded software, maybe high performance stuff, etc.

> "Not only are you responsible for the work getting done, you also now own how it gets done, and therefore are responsible for continual optimization and maximal automation ..." -Proton CTO

The article talks about why humans are getting in the way of AI automation, and I think the main problem is reflected in that statement. Engineers are responsible of a thin vertical of the entire business, and I find it ironic and "rich" for the leadership to constantly demand more of engineers while at the same time scratching their heads why it's not really working.

I actually think AI is going to throw a wrench into this because it's not JUST going to automate engineering, it's going to empower high level engineers to do more... at the cost of handling AI and making sure it works smoothly, and this is going to have a huge overhead.

So I think the two options are, let engineers specialize, become more productive but also more detached from what the business does, creating automation workflows that others use... or try to add AI on top of everything engineers already do and watch your org crash and burn as no one can manage the chaos.

It's the corporate approach to game development, which should be creative driven. I think it's really hard for a large company to remain creative, it grows in bureaucracy and non-contributors by default.

Wow, literally everything in the supermarket has detrimental effects. Either it has too much sugar or a sugar substitute or genetically modified. We're seriously failing as a civilization when it comes to nutrition in modern times.

It used to be only big tech but now large companies are sprinting towards Black mirror episodes.

Yeah that's the status quo. No surprise it's Grok that deviates.

It's interesting that the current AI revolution began with transformers that came out of Google who was trying to apply AI to search, and now that seems to be the an unpopular use for it. I think it's one of the better use cases for it. It's great at compressing knowledge and if it can search the web and summarize things I don't see much of a downside. As far as making a profit, I don't see why they wouldn't charge a small amount for it, and that way they're not shoving everyone to use it.

I should rephrase on the first one. We only have one source of reference for what thinking is and where it happens.

On the second one, yes there can be multiple ways that consciousness can manifest in wildly different systems, but what theory do we have that would validate these other different kinds of consciousness? Without that theory for validation the safer bet is to stick with what we already have a reference to, unless the claim is that consciousness is such an easy algorithm it can easily be stumbled upon by all kinds of systems, i.e. a bunch of matrix multiplications that doesn't even run in real-time (it executes prompts and then stops). I think that is not likely in my opinion.

We evolved to handle light as a sensory stream of data, whether a few are born without part of the system (usually the sensors and not the processing system) is something else. My point is more that we don't really know. The exceptions are not disproving how biological systems evolved.

I would include processing real-time sensory data, i.e. light, building a real-time model of a self and the world, a unified way to perceive, react, and learn from the sensory data. This would be for consciousness in general. Once we're talking about human-level consciousness where language is involved, the list is longer, with theory of mind, ability to construct models of others fairly quickly, and ability to navigate social situations.

Why those properties though? What's so special about them? I just have a longer list of things it would need to satisfy before I can say faking doesn't matter.

Right, but we don't know how much that is necessary for a conscious system. Maybe processing light has certain properties necessary for consciousness. If we don't know this then for me the safe bet is to say it is not conscious unless the system is processing at least a similar stream of information that maps to light waves.

That's the question, it's the Chinese room or philosophical zombie question. How can we tell them apart... but we're not even there yet.

If you put an LLM on your phone and talk to it, it's obvious it's not human. It doesn't have the same social context, it doesn't respond as quickly, it clearly won't form a theory of m ind connection like two humans socializing. So then why would we jump to the conclusion there is something there when the only example we have are humans (that use language and are conscious)?

It is a different signal conversion, but the question is whether that difference is meaningful or not. Light waves are much different than tokens. I think it affects the overall system of our brains much more than we currently know. Having access to so much data has probably shaped biological brains at its "core" (initial evolution stages).

The starting point is the consciousness we already know. That is ourselves, which most people then extend to others, however it's not hard to then extended to at the very least mammals. I then extend this to most animals (whether it's 100% is irrelevant for this point). That extension from my consciousness to all animals is a small logical leap based on biology and our shared tree of life... however it doesn't mean other consciousnesses are not possible. It's more so that they would require a lot more evidence and a proper theory of C. in order to validate. Without that theory we can only point at similar C., of which LLMs are not displaying. They are showing a different kind of "cognition" if we can even call it that (I do think it's debatable).

So it's more a hueristic. We know one kind of C., therefore that's what we should look for. Statistically it's probable that there are many more ways to show similarities with conscious behavior which are NOT conscious, then it is for a divergent behavior to happen to be conscious. In other words, there are more ways to fake similar behavior than to land on an actual conscious behavior that is somehow much different than what we know.

I just don't know if "similar" is good enough. We know what thinking is based on biological brains, so if we are to conclude it happens in machines, the experiment should have identical results, or it's more probable that it's something completely different.

The way around this is to have a predictive theory of how thoughts work, and if that is proven true, then use that to analyze the LLMs. So far we have no such theory so I think the only tool is to look at the exact behavior match.